An update on THORPEX-related research in data assimilation and observing strategies
An update on THORPEX-related research in data assimilation and observing strategies
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DOI：
10.5194/npg-15-81-2008
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发表时间：
2008-02
影响因子：
2.2
通讯作者：
F. Rabier;P. Gauthier;C. Cardinali;R. Langland;M. Tsyrulnikov;A. Lorenc;P. Steinle;R. Gelaro
中科院分区：
文献类型：
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作者：
F. Rabier;P. Gauthier;C. Cardinali;R. Langland;M. Tsyrulnikov;A. Lorenc;P. Steinle;R. Gelaro
国际计划“THORPEX：世界天气研究计划”提供了一个框架，通过学术机构、业务预报中心和预报产品用户之间的国际合作，来应对提高高影响天气预报技能的挑战。THORPEX数据同化和观测策略工作组（DAOS-WG）的目标是双重的。主要目标是评估观测的影响和各种确定目标的方法，为观测活动和全球观测系统的配置提供指导。第二个目标是建立一个数据同化的最佳框架，包括目标观测、卫星数据、背景误差协方差和质量控制等方面。2003年大西洋THORPEX区域活动（ATReC）在技术上非常成功，为测试目标问题提供了有价值的数据集。已经进行了各种数据影响实验，显示了目标观测的小但非常轻微的积极影响。DAOS-WG的项目包括在国际极地年的背景下进行AMMA实地试验，并通过比较几个小组之间的预测对观测的敏感性，为未来在太平洋的THORPEX-PARC实地活动做准备。
The international programme "THORPEX: a World Weather Research Programme" provides a framework in which to tackle the challenge of improving the forecast skill of high-impact weather through international collaboration between academic institutions, operational forecast centres, and users of forecast products. The objectives of the THORPEX Data Assimilation and Observation Strategy Working Group (DAOS-WG) are two-fold. The primary goal is to assess the impact of observations and various targeting methods to provide guidance for observation campaigns and for the configuration of the Global Observing System. The secondary goal is to setup an optimal framework for data assimilation, including aspects such as targeted observations, satellite data, background error covariances and quality control. The Atlantic THORPEX Regional campaign, ATReC, in 2003, has been very successful technically and has provided valuable datasets to test targeting issues. Various data impact experiments have been performed, showing a small but very slightly positive impact of targeted observations. Projects of the DAOS-WG include working on the AMMA field experiment, in the context of IPY and to prepare the future THORPEX-PARC field campaign in the Pacific by comparing sensitivity of the forecasts to observations between several groups.